利用轨迹数据进行道路网更新及轨迹融合
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摘要
随着经济的快速发展,城市道路网更新迅速,道路网数据对于交通规划和城市发展具有重要意义。本文提出了一种基于轨迹数据的道路更新及轨迹融合方法。首先使用几何最短距离分析和隐马尔科夫模型来提取失配轨迹,然后使用基于Delaunay三角网和基于山脊线相结合的轨迹融合方法,提取出新增道路骨架线,对得到的骨架线进行显化和延长处理,使其融合到现有道路网中。试验结果表明,采用基于Delaunay三角网和基于山脊线相结合的轨迹融合方法,能够提高新增道路的生成效率,改善道路网的更新效果。
With the rapid development of economy,the updating frequency of urban road network has increased rapidly. Road network data is of great significance for traffic planning and urban development.In this paper,a road updating approach based on GPS trajectory data is proposed.Firstly,the geometric shortest distance analysis and hidden Markov model analysis are used to extract the mismatched trajectory.These mismatched trajectories indicate road segments previously not available and include key information of the newly built road.To build new road network,the skeleton line of the new road is extracted by combining the Delaunay triangulation and the ridge line.Then the skeleton lines are simplified and extended to make the incremental update to the existing road network.The experimental results show that the combination of Delaunay triangulation and ridge line based on newly build trajectories can improve the generating efficiency of new roads and improve the updating effect of road network.The method indicates that the proposed approach can be used for incremental updating of large-scale road network.
With the rapid development of economy,the updating frequency of urban road network has increased rapidly. Road network data is of great significance for traffic planning and urban development.In this paper,a road updating approach based on GPS trajectory data is proposed.Firstly,the geometric shortest distance analysis and hidden Markov model analysis are used to extract the mismatched trajectory.These mismatched trajectories indicate road segments previously not available and include key information of the newly built road.To build new road network,the skeleton line of the new road is extracted by combining the Delaunay triangulation and the ridge line.Then the skeleton lines are simplified and extended to make the incremental update to the existing road network.The experimental results show that the combination of Delaunay triangulation and ridge line based on newly build trajectories can improve the generating efficiency of new roads and improve the updating effect of road network.The method indicates that the proposed approach can be used for incremental updating of large-scale road network.
引文
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[14]唐炉亮,刘章,杨雪,等.符合认知规律的时空轨迹融合与路网生成方法[J].测绘学报,2015,44(11):1271-1276.
[15]MOREIRA-MATIAS L,GAMA J,FERREIRA M,et al.Predicting Taxi-passenger Demand Using Streaming Data[J].IEEE Transactions on Intelligent Transportation Systems,2013,14(3):1393-1402.
[2]吴涛,向隆刚,龚健雅.路网更新的轨迹-地图匹配方法[J].测绘学报,2017,46(4):507-515.
[3]AGAMENNONI G,NIETO J I,NEBOT E M.Robust Inference of Principal Road Paths for Intelligent Transportation Systems[J].IEEE Transactions on Intelligent Transportation Systems,2011,12(1):298-308.
[4]WU T,XIANG L,GONG J.Updating Road Networks by Local Renewal from GPS Trajectories[J].ISPRS International Journal of Geo-Information,2016,5(9):163.
[5]NIEHOEFER B,BURDA R,WIETFELD C,et al.GPS Community Map Generation for Enhanced Routing Methods Based on Trace-collection by Mobile Phones[C]∥2009 First International Conference on Advances in Satellite and Space Communications.[S.l.]:IEEE,2009.
[6]ZHANG L,THIEMANN F,SESTER M.Integration of GPS Traces with Road Map[C]∥Proceedings of the Third International Workshop on Computational Transportation Science.[S.l.]:ACM,2010.
[7]BIAGIONI J,ERIKSSON J.Inferring Road Maps from Global Positioning System Traces[J].Transportation Research Record:Journal of the Transportation Research Board,2012,2291(1):61-71.
[8]SHI W,SHEN S,LIU Y.Automatic Generation of Road Network Map from Massive GPS,Vehicle Trajectories[C]∥12th International IEEE Conference on Intelligent Transportation Systems.[S.l.]:IEEE,2009.
[9]CHEN C,CHENG Y.Roads Digital Map Generation with Multi-track GPS Data[C]∥2008 International Workshop on Geoscience and Remote Sensing.[S.l.]:IEEE,2008.
[10]王振华,胡翔云,单杰.众源GPS浮动车数据中城市道路中心线分级提取的栅格化方法[J].测绘通报,2015(8):22-24,34.
[11]ESTIVILL-CASTRO V,LEE I.Multi-level Clustering and Its Visualization for Exploratory Spatial Analysis[J].Geo Informatica,2002,6(2):123-152.
[12]杨伟,艾廷华.基于车辆轨迹大数据的道路网更新方法研究[J].计算机研究与发展,2016,53(12):2681-2693.
[13]杨伟,艾廷华.运用约束Delaunay三角网从众源轨迹线提取道路边界[J].测绘学报,2017,46(2):237-245.
[14]唐炉亮,刘章,杨雪,等.符合认知规律的时空轨迹融合与路网生成方法[J].测绘学报,2015,44(11):1271-1276.
[15]MOREIRA-MATIAS L,GAMA J,FERREIRA M,et al.Predicting Taxi-passenger Demand Using Streaming Data[J].IEEE Transactions on Intelligent Transportation Systems,2013,14(3):1393-1402.